Applying Causal Inference in Educational Data Mining: A Pilot Study

Abstract

Understanding the reasons that leads students to succeed during their course is a challenge for every Institution of Education, independently of the modality of teaching and learning adopted. In this paper we use the theory of Causal Inference for analyzing the main factors that causes the success, or failure, of an engineering student enrolled in an online course of Algorithm. We used data extracted from the Learning Management System Moodle and, after preprocessing the dataset, analyzed the actions performed by the students during the six months (20 weeks) that the online course lasted. We concluded that before submitting an evaluation activity to be assessed, it is important that students analyze the problem thoroughly. Students that took a little bit longer to submit their work got more chances to be approved.

A Survey of Deep Causal Models and Their Industrial Applications

“…Speciőcally, different posttest items measuring different skills. Carvalho [160] conductd causal effect estimation on students' online behavior patterns using a different toolkit, GeNIe3, using data from a learning management system. Chen et al [161] developed a causal discovery framework that utilized TETRAD [162].…”